AutoML Leaderboard
AutoML Performance

AutoML Performance Boxplot

Features Importance

Spearman Correlation of Models

Summary of 5_Default_NeuralNetwork
<< Go back
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
1.5 seconds
Metric details
|
score |
threshold |
| logloss |
0.73664 |
nan |
| auc |
0.46388 |
nan |
| f1 |
0.498361 |
0.0152774 |
| accuracy |
0.615721 |
0.505195 |
| precision |
0.344828 |
0.347606 |
| recall |
1 |
0.0152774 |
| mcc |
0.0278636 |
0.347606 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.73664 |
nan |
| auc |
0.46388 |
nan |
| f1 |
0.137255 |
0.505195 |
| accuracy |
0.615721 |
0.505195 |
| precision |
0.269231 |
0.505195 |
| recall |
0.0921053 |
0.505195 |
| mcc |
-0.0476126 |
0.505195 |
Confusion matrix (at threshold=0.505195)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
134 |
19 |
| Labeled as 1 |
69 |
7 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

<< Go back
Summary of 1_Baseline
<< Go back
Baseline Classifier (Baseline)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
0.2 seconds
Metric details
|
score |
threshold |
| logloss |
0.635509 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.498361 |
0.300437 |
| accuracy |
0.331878 |
0.300437 |
| precision |
0.331878 |
0.300437 |
| recall |
1 |
0.300437 |
| mcc |
0 |
0.300437 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.635509 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.498361 |
0.300437 |
| accuracy |
0.331878 |
0.300437 |
| precision |
0.331878 |
0.300437 |
| recall |
1 |
0.300437 |
| mcc |
0 |
0.300437 |
Confusion matrix (at threshold=0.300437)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
0 |
153 |
| Labeled as 1 |
0 |
76 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

<< Go back
Summary of Ensemble
<< Go back
Ensemble structure
| Model |
Weight |
| 3_Linear |
1 |
| 4_Default_Xgboost |
3 |
| 6_Default_RandomForest |
2 |
Metric details
|
score |
threshold |
| logloss |
0.638378 |
nan |
| auc |
0.577829 |
nan |
| f1 |
0.510638 |
0.335527 |
| accuracy |
0.689956 |
0.537046 |
| precision |
0.777778 |
0.537046 |
| recall |
1 |
0.249642 |
| mcc |
0.191527 |
0.537046 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.638378 |
nan |
| auc |
0.577829 |
nan |
| f1 |
0.164706 |
0.537046 |
| accuracy |
0.689956 |
0.537046 |
| precision |
0.777778 |
0.537046 |
| recall |
0.0921053 |
0.537046 |
| mcc |
0.191527 |
0.537046 |
Confusion matrix (at threshold=0.537046)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
151 |
2 |
| Labeled as 1 |
69 |
7 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

<< Go back
Summary of 2_DecisionTree
<< Go back
Decision Tree
- n_jobs: -1
- criterion: gini
- max_depth: 3
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
5.9 seconds
Metric details
|
score |
threshold |
| logloss |
0.745524 |
nan |
| auc |
0.517931 |
nan |
| f1 |
0.498361 |
0.113445 |
| accuracy |
0.650655 |
0.517571 |
| precision |
0.40625 |
0.41044 |
| recall |
1 |
0.113445 |
| mcc |
0.0636555 |
0.41044 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.745524 |
nan |
| auc |
0.517931 |
nan |
| f1 |
0.130435 |
0.517571 |
| accuracy |
0.650655 |
0.517571 |
| precision |
0.375 |
0.517571 |
| recall |
0.0789474 |
0.517571 |
| mcc |
0.0250989 |
0.517571 |
Confusion matrix (at threshold=0.517571)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
143 |
10 |
| Labeled as 1 |
70 |
6 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

<< Go back
Summary of 6_Default_RandomForest
<< Go back
Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
11.4 seconds
Metric details
|
score |
threshold |
| logloss |
0.64123 |
nan |
| auc |
0.566262 |
nan |
| f1 |
0.498361 |
0.0843244 |
| accuracy |
0.689956 |
0.523772 |
| precision |
0.608696 |
0.523772 |
| recall |
1 |
0.0843244 |
| mcc |
0.19643 |
0.523772 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.64123 |
nan |
| auc |
0.566262 |
nan |
| f1 |
0.282828 |
0.523772 |
| accuracy |
0.689956 |
0.523772 |
| precision |
0.608696 |
0.523772 |
| recall |
0.184211 |
0.523772 |
| mcc |
0.19643 |
0.523772 |
Confusion matrix (at threshold=0.523772)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
144 |
9 |
| Labeled as 1 |
62 |
14 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

<< Go back
Summary of 4_Default_Xgboost
<< Go back
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
50.5 seconds
Metric details
|
score |
threshold |
| logloss |
0.682156 |
nan |
| auc |
0.556674 |
nan |
| f1 |
0.498361 |
0.422421 |
| accuracy |
0.68559 |
0.51789 |
| precision |
0.666667 |
0.51789 |
| recall |
1 |
0.422421 |
| mcc |
0.167192 |
0.51789 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.682156 |
nan |
| auc |
0.556674 |
nan |
| f1 |
0.181818 |
0.51789 |
| accuracy |
0.68559 |
0.51789 |
| precision |
0.666667 |
0.51789 |
| recall |
0.105263 |
0.51789 |
| mcc |
0.167192 |
0.51789 |
Confusion matrix (at threshold=0.51789)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
149 |
4 |
| Labeled as 1 |
68 |
8 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

<< Go back
Summary of 3_Linear
<< Go back
Logistic Regression (Linear)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
3.0 seconds
Metric details
|
score |
threshold |
| logloss |
0.686749 |
nan |
| auc |
0.549794 |
nan |
| f1 |
0.508834 |
0.0960719 |
| accuracy |
0.676856 |
0.519582 |
| precision |
0.538462 |
0.690156 |
| recall |
1 |
0.0162395 |
| mcc |
0.188675 |
0.519582 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.686749 |
nan |
| auc |
0.549794 |
nan |
| f1 |
0.362069 |
0.519582 |
| accuracy |
0.676856 |
0.519582 |
| precision |
0.525 |
0.519582 |
| recall |
0.276316 |
0.519582 |
| mcc |
0.188675 |
0.519582 |
Confusion matrix (at threshold=0.519582)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
134 |
19 |
| Labeled as 1 |
55 |
21 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

<< Go back